Overview

Dataset statistics

Number of variables14
Number of observations52560
Missing cells939
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 5 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
blade_angle is highly overall correlated with Rear bearing temperature (°C)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Rotor speed (RPM) and 2 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
# Date and time has unique valuesUnique
blade_angle has 20809 (39.6%) zerosZeros
Rotor speed (RPM) has 1125 (2.1%) zerosZeros

Reproduction

Analysis started2023-07-08 11:54:02.578522
Analysis finished2023-07-08 11:54:19.219434
Duration16.64 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

# Date and time
Date

Distinct52560
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size410.8 KiB
Minimum2019-01-01 00:00:00
Maximum2019-12-31 23:50:00
2023-07-08T17:24:19.272425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:19.362067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52296
Distinct (%)99.6%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean567.8977
Minimum-16.147116
Maximum2078.6285
Zeros1
Zeros (%)< 0.1%
Negative5409
Negative (%)10.3%
Memory size410.8 KiB
2023-07-08T17:24:19.466095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-16.147116
5-th percentile-1.8857358
Q1119.84645
median368.39696
Q3825.08232
95-th percentile1944.4743
Maximum2078.6285
Range2094.7756
Interquartile range (IQR)705.23587

Descriptive statistics

Standard deviation580.78886
Coefficient of variation (CV)1.0226998
Kurtosis0.4091358
Mean567.8977
Median Absolute Deviation (MAD)303.06972
Skewness1.1843812
Sum29807814
Variance337315.7
MonotonicityNot monotonic
2023-07-08T17:24:19.675771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.585028535 4
 
< 0.1%
-1.450053036 3
 
< 0.1%
-3.158853531 3
 
< 0.1%
-1.478862041 3
 
< 0.1%
-1.598899537 3
 
< 0.1%
-0.7613045216 3
 
< 0.1%
-1.484197038 3
 
< 0.1%
-1.732274544 3
 
< 0.1%
-1.461256534 3
 
< 0.1%
-1.383899033 3
 
< 0.1%
Other values (52286) 52457
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
-16.14711571 1
< 0.1%
-14.77205367 1
< 0.1%
-14.52774999 1
< 0.1%
-14.29665489 1
< 0.1%
-14.24649096 1
< 0.1%
-14.03430805 1
< 0.1%
-13.98847008 1
< 0.1%
-13.81120019 1
< 0.1%
-12.9386721 1
< 0.1%
-12.88180908 1
< 0.1%
ValueCountFrequency (%)
2078.62851 1
< 0.1%
2077.037 1
< 0.1%
2075.845068 1
< 0.1%
2074.531012 1
< 0.1%
2073.768561 1
< 0.1%
2072.981598 1
< 0.1%
2072.459827 1
< 0.1%
2072.332601 1
< 0.1%
2071.526566 1
< 0.1%
2071.323407 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52483
Distinct (%)> 99.9%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean204.38761
Minimum0.0087556085
Maximum359.99737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:19.766621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0087556085
5-th percentile30.892338
Q1147.45596
median222.21096
Q3268.31399
95-th percentile330.2877
Maximum359.99737
Range359.98861
Interquartile range (IQR)120.85804

Descriptive statistics

Standard deviation89.800312
Coefficient of variation (CV)0.43936279
Kurtosis-0.53017552
Mean204.38761
Median Absolute Deviation (MAD)54.326935
Skewness-0.56377456
Sum10727897
Variance8064.096
MonotonicityNot monotonic
2023-07-08T17:24:19.861459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
277.4647827 2
 
< 0.1%
306.3887939 2
 
< 0.1%
276.9495544 2
 
< 0.1%
282.1563416 2
 
< 0.1%
276.950592 2
 
< 0.1%
272.6219667 1
 
< 0.1%
271.5558739 1
 
< 0.1%
267.6872632 1
 
< 0.1%
271.0586562 1
 
< 0.1%
269.5059775 1
 
< 0.1%
Other values (52473) 52473
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
0.008755608462 1
< 0.1%
0.01073139114 1
< 0.1%
0.01450898622 1
< 0.1%
0.02724797514 1
< 0.1%
0.03075436255 1
< 0.1%
0.03800275813 1
< 0.1%
0.06816547986 1
< 0.1%
0.07451513504 1
< 0.1%
0.08148678417 1
< 0.1%
0.08569295006 1
< 0.1%
ValueCountFrequency (%)
359.9973687 1
< 0.1%
359.9917878 1
< 0.1%
359.991333 1
< 0.1%
359.9867208 1
< 0.1%
359.957253 1
< 0.1%
359.9526609 1
< 0.1%
359.9456787 1
< 0.1%
359.9388308 1
< 0.1%
359.9271398 1
< 0.1%
359.912685 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct14196
Distinct (%)27.0%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean203.63813
Minimum0.080293669
Maximum359.95383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:19.962742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.080293669
5-th percentile28.654795
Q1147.19113
median222.92401
Q3269.0217
95-th percentile329.70122
Maximum359.95383
Range359.87354
Interquartile range (IQR)121.83057

Descriptive statistics

Standard deviation90.540333
Coefficient of variation (CV)0.44461386
Kurtosis-0.55045272
Mean203.63813
Median Absolute Deviation (MAD)54.878326
Skewness-0.57421807
Sum10688558
Variance8197.5519
MonotonicityNot monotonic
2023-07-08T17:24:20.056518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
312.9248962 151
 
0.3%
225.1191406 145
 
0.3%
198.7775879 123
 
0.2%
72.55846405 120
 
0.2%
234.9972229 113
 
0.2%
314.0224609 108
 
0.2%
226.2158508 107
 
0.2%
220.7288818 103
 
0.2%
260.2417297 101
 
0.2%
51.70483398 98
 
0.2%
Other values (14186) 51319
97.6%
ValueCountFrequency (%)
0.08029366936 1
 
< 0.1%
0.1182251051 26
< 0.1%
0.1182963774 1
 
< 0.1%
0.1188354418 3
 
< 0.1%
0.1188659668 10
 
< 0.1%
0.1188666895 2
 
< 0.1%
0.1188971922 1
 
< 0.1%
0.1189067364 8
 
< 0.1%
0.1190185472 7
 
< 0.1%
0.1191661358 16
< 0.1%
ValueCountFrequency (%)
359.9538346 1
< 0.1%
359.9223584 1
< 0.1%
359.8891907 1
< 0.1%
359.8640617 1
< 0.1%
359.7876982 1
< 0.1%
359.769761 1
< 0.1%
359.7548619 1
< 0.1%
359.736221 1
< 0.1%
359.7067864 1
< 0.1%
359.6829025 1
< 0.1%

blade_angle
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18635
Distinct (%)35.5%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.5027314
Minimum0
Maximum92.496666
Zeros20809
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:20.156424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.10066368
Q31.3352229
95-th percentile44.996667
Maximum92.496666
Range92.496666
Interquartile range (IQR)1.3352229

Descriptive statistics

Standard deviation14.257519
Coefficient of variation (CV)2.5909895
Kurtosis10.420471
Mean5.5027314
Median Absolute Deviation (MAD)0.10066368
Skewness3.1396589
Sum288827.37
Variance203.27686
MonotonicityNot monotonic
2023-07-08T17:24:20.248488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20809
39.6%
44.99666723 3645
 
6.9%
1.49666667 1253
 
2.4%
0.02483306751 938
 
1.8%
0.04966621902 427
 
0.8%
0.07449944518 254
 
0.5%
89.99666595 179
 
0.3%
0.4966666698 169
 
0.3%
0.09933273667 167
 
0.3%
1.471666941 141
 
0.3%
Other values (18625) 24506
46.6%
ValueCountFrequency (%)
0 20809
39.6%
0.0001666666552 3
 
< 0.1%
0.0001666666622 9
 
< 0.1%
0.0001754385885 1
 
< 0.1%
0.0001754385918 3
 
< 0.1%
0.0001960784262 1
 
< 0.1%
0.0003333333104 1
 
< 0.1%
0.0003333333201 10
 
< 0.1%
0.0003333333244 7
 
< 0.1%
0.000350877177 1
 
< 0.1%
ValueCountFrequency (%)
92.49666595 47
0.1%
92.48149951 1
 
< 0.1%
92.32549866 1
 
< 0.1%
92.23666636 1
 
< 0.1%
92.22666677 1
 
< 0.1%
92.22666423 7
 
< 0.1%
92.22133376 1
 
< 0.1%
92.21666972 14
 
< 0.1%
92.20333099 4
 
< 0.1%
92.19999952 1
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

Distinct36125
Distinct (%)68.8%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean64.475873
Minimum13.125
Maximum75.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:20.340496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13.125
5-th percentile45.11
Q163.751876
median67.156382
Q369.097498
95-th percentile71.2025
Maximum75.01
Range61.885
Interquartile range (IQR)5.3456227

Descriptive statistics

Standard deviation8.1369942
Coefficient of variation (CV)0.12620216
Kurtosis6.3565535
Mean64.475873
Median Absolute Deviation (MAD)2.3672255
Skewness-2.3926163
Sum3384209.6
Variance66.210674
MonotonicityNot monotonic
2023-07-08T17:24:20.428104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.89999962 12
 
< 0.1%
68.75 12
 
< 0.1%
67.43500023 11
 
< 0.1%
69 11
 
< 0.1%
68.9375 10
 
< 0.1%
67.31999969 10
 
< 0.1%
68.98500023 10
 
< 0.1%
68.49249954 9
 
< 0.1%
67.75 9
 
< 0.1%
69.46750031 9
 
< 0.1%
Other values (36115) 52385
99.7%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
13.125 1
< 0.1%
13.21249962 1
< 0.1%
13.33500004 1
< 0.1%
13.37250042 1
< 0.1%
13.39999962 1
< 0.1%
13.46500015 1
< 0.1%
13.5975008 1
< 0.1%
13.66250038 1
< 0.1%
13.70499992 1
< 0.1%
13.89999962 1
< 0.1%
ValueCountFrequency (%)
75.01000023 1
< 0.1%
74.80750084 1
< 0.1%
74.56750031 1
< 0.1%
74.54999924 1
< 0.1%
74.51999931 1
< 0.1%
74.43157999 1
< 0.1%
74.42500153 1
< 0.1%
74.40500183 1
< 0.1%
74.39473885 1
< 0.1%
74.39250183 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51305
Distinct (%)97.7%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10.489858
Minimum0
Maximum15.312472
Zeros1125
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:20.525010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.63181662
Q19.2064733
median10.574084
Q313.384548
95-th percentile15.148088
Maximum15.312472
Range15.312472
Interquartile range (IQR)4.1780748

Descriptive statistics

Standard deviation3.8657472
Coefficient of variation (CV)0.36852236
Kurtosis1.3370155
Mean10.489858
Median Absolute Deviation (MAD)1.5819568
Skewness-1.2131278
Sum550591.67
Variance14.944001
MonotonicityNot monotonic
2023-07-08T17:24:20.622155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1125
 
2.1%
0.0110000018 7
 
< 0.1%
0.01200000197 4
 
< 0.1%
0.02250000369 3
 
< 0.1%
0.01050000242 3
 
< 0.1%
0.0250000041 3
 
< 0.1%
0.01450000238 3
 
< 0.1%
0.01250000205 3
 
< 0.1%
0.0220000043 2
 
< 0.1%
9.826178551 2
 
< 0.1%
Other values (51295) 51333
97.7%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
0 1125
2.1%
0.000483000098 1
 
< 0.1%
0.007436001208 1
 
< 0.1%
0.007528502028 1
 
< 0.1%
0.01050000242 3
 
< 0.1%
0.0110000018 7
 
< 0.1%
0.01105263457 1
 
< 0.1%
0.01111400197 1
 
< 0.1%
0.01117700175 1
 
< 0.1%
0.01150000188 1
 
< 0.1%
ValueCountFrequency (%)
15.31247221 1
< 0.1%
15.31193528 1
< 0.1%
15.29943305 1
< 0.1%
15.29218452 1
< 0.1%
15.29162742 1
< 0.1%
15.29040658 1
< 0.1%
15.28961188 1
< 0.1%
15.286452 1
< 0.1%
15.28637628 1
< 0.1%
15.28574838 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52463
Distinct (%)> 99.9%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1244.5095
Minimum-41.413587
Maximum1815.633
Zeros0
Zeros (%)0.0%
Negative519
Negative (%)1.0%
Memory size410.8 KiB
2023-07-08T17:24:20.724446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-41.413587
5-th percentile75.606592
Q11093.4911
median1255.2035
Q31587.2622
95-th percentile1795.1296
Maximum1815.633
Range1857.0466
Interquartile range (IQR)493.77103

Descriptive statistics

Standard deviation457.79552
Coefficient of variation (CV)0.36785218
Kurtosis1.3510334
Mean1244.5095
Median Absolute Deviation (MAD)187.53065
Skewness-1.2197756
Sum65321814
Variance209576.74
MonotonicityNot monotonic
2023-07-08T17:24:20.818610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1065.858032 2
 
< 0.1%
1256.713909 2
 
< 0.1%
1069.023682 2
 
< 0.1%
1799.450054 2
 
< 0.1%
1121.412809 2
 
< 0.1%
1113.646414 2
 
< 0.1%
1796.671383 2
 
< 0.1%
1069.979126 2
 
< 0.1%
1250.973267 2
 
< 0.1%
1790.915096 2
 
< 0.1%
Other values (52453) 52468
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
-41.41358703 1
< 0.1%
-10.80355365 1
< 0.1%
-1.099502312 1
< 0.1%
-1.072384944 1
< 0.1%
-1.06880878 1
< 0.1%
-1.067865374 1
< 0.1%
-1.053867878 1
< 0.1%
-1.05358269 1
< 0.1%
-1.030063413 1
< 0.1%
-1.021621519 1
< 0.1%
ValueCountFrequency (%)
1815.632978 1
< 0.1%
1814.167711 1
< 0.1%
1813.615391 1
< 0.1%
1813.536261 1
< 0.1%
1813.031496 1
< 0.1%
1812.909313 1
< 0.1%
1812.195351 1
< 0.1%
1812.118486 1
< 0.1%
1811.736343 1
< 0.1%
1810.063156 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

Distinct32042
Distinct (%)61.0%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11.140951
Minimum-0.57750005
Maximum35.9925
Zeros0
Zeros (%)0.0%
Negative127
Negative (%)0.2%
Memory size410.8 KiB
2023-07-08T17:24:20.920007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.57750005
5-th percentile3.07
Q16.849375
median10.41
Q315.056875
95-th percentile20.871625
Maximum35.9925
Range36.57
Interquartile range (IQR)8.2075002

Descriptive statistics

Standard deviation5.6223122
Coefficient of variation (CV)0.5046528
Kurtosis0.084132724
Mean11.140951
Median Absolute Deviation (MAD)3.9899996
Skewness0.51968938
Sum584766.24
Variance31.610394
MonotonicityNot monotonic
2023-07-08T17:24:21.014606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.5 85
 
0.2%
7.400000095 70
 
0.1%
6.699999809 68
 
0.1%
10.60000038 66
 
0.1%
6.300000191 64
 
0.1%
6.599999905 63
 
0.1%
7.199999809 61
 
0.1%
7.900000095 59
 
0.1%
6.800000191 56
 
0.1%
7.699999809 55
 
0.1%
Other values (32032) 51841
98.6%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
-0.5775000453 1
< 0.1%
-0.5657894611 1
< 0.1%
-0.5285714269 1
< 0.1%
-0.5275000334 1
< 0.1%
-0.5074999928 1
< 0.1%
-0.4925000072 1
< 0.1%
-0.4850000143 1
< 0.1%
-0.4774999917 2
< 0.1%
-0.4724999964 2
< 0.1%
-0.4675000012 1
< 0.1%
ValueCountFrequency (%)
35.99250011 2
< 0.1%
35.92000103 1
< 0.1%
35.84250126 1
< 0.1%
35.79750099 1
< 0.1%
35.74000092 1
< 0.1%
35.73750114 1
< 0.1%
35.7026323 1
< 0.1%
35.66250019 1
< 0.1%
35.475 1
< 0.1%
35.27500019 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

Distinct37909
Distinct (%)72.2%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean66.000151
Minimum14.165
Maximum82.794736
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:21.115335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.165
5-th percentile44.29175
Q162.5575
median70.047369
Q372.694999
95-th percentile74.265
Maximum82.794736
Range68.629736
Interquartile range (IQR)10.137499

Descriptive statistics

Standard deviation9.7374297
Coefficient of variation (CV)0.14753648
Kurtosis2.8782353
Mean66.000151
Median Absolute Deviation (MAD)3.4873685
Skewness-1.719638
Sum3464215.9
Variance94.817538
MonotonicityNot monotonic
2023-07-08T17:24:21.207569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.39999962 15
 
< 0.1%
29.60000038 12
 
< 0.1%
73.50999985 10
 
< 0.1%
73.47749939 10
 
< 0.1%
73 9
 
< 0.1%
73.75750008 9
 
< 0.1%
73.48499985 9
 
< 0.1%
71.07500038 9
 
< 0.1%
73.875 9
 
< 0.1%
72.40000153 9
 
< 0.1%
Other values (37899) 52387
99.7%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
14.16499996 1
< 0.1%
14.18499947 1
< 0.1%
14.22999954 1
< 0.1%
14.30000019 1
< 0.1%
14.35750008 1
< 0.1%
14.40999985 1
< 0.1%
14.48499966 1
< 0.1%
14.60000038 1
< 0.1%
14.60750008 1
< 0.1%
14.69999981 1
< 0.1%
ValueCountFrequency (%)
82.79473596 1
< 0.1%
82.04249954 1
< 0.1%
81.7593751 1
< 0.1%
81.3166665 1
< 0.1%
81.28947449 1
< 0.1%
80.72749939 1
< 0.1%
80.61499901 1
< 0.1%
80.49249916 1
< 0.1%
80.38499985 1
< 0.1%
80.344737 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

Distinct52485
Distinct (%)> 99.9%
Missing73
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean69.503703
Minimum2.0760748
Maximum256.35336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:21.303766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.0760748
5-th percentile3.9595409
Q143.979824
median66.449416
Q394.860385
95-th percentile136.87327
Maximum256.35336
Range254.27729
Interquartile range (IQR)50.880561

Descriptive statistics

Standard deviation38.543116
Coefficient of variation (CV)0.55454766
Kurtosis-0.070351841
Mean69.503703
Median Absolute Deviation (MAD)25.254549
Skewness0.35501245
Sum3648040.9
Variance1485.5718
MonotonicityNot monotonic
2023-07-08T17:24:21.399541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.80155945 2
 
< 0.1%
64.19764709 2
 
< 0.1%
54.78525162 1
 
< 0.1%
43.48230529 1
 
< 0.1%
55.44305544 1
 
< 0.1%
49.53755081 1
 
< 0.1%
64.93752661 1
 
< 0.1%
60.97089272 1
 
< 0.1%
64.91128392 1
 
< 0.1%
62.1121562 1
 
< 0.1%
Other values (52475) 52475
99.8%
(Missing) 73
 
0.1%
ValueCountFrequency (%)
2.076074839 1
< 0.1%
2.114367247 1
< 0.1%
2.121529579 1
< 0.1%
2.139735699 1
< 0.1%
2.210576774 1
< 0.1%
2.218492707 1
< 0.1%
2.245212317 1
< 0.1%
2.250659728 1
< 0.1%
2.286777496 1
< 0.1%
2.322940826 1
< 0.1%
ValueCountFrequency (%)
256.3533607 1
< 0.1%
251.011292 1
< 0.1%
250.8053684 1
< 0.1%
248.0438828 1
< 0.1%
240.631451 1
< 0.1%
236.2135605 1
< 0.1%
233.2323858 1
< 0.1%
231.088124 1
< 0.1%
230.8741627 1
< 0.1%
229.533655 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct52352
Distinct (%)99.7%
Missing72
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.9315454
Minimum0.17403787
Maximum22.935832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:21.611771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.17403787
5-th percentile2.2438735
Q14.1183394
median5.68997
Q37.3512302
95-th percentile10.644339
Maximum22.935832
Range22.761794
Interquartile range (IQR)3.2328907

Descriptive statistics

Standard deviation2.610275
Coefficient of variation (CV)0.4400666
Kurtosis1.3636051
Mean5.9315454
Median Absolute Deviation (MAD)1.6167205
Skewness0.82928972
Sum311334.96
Variance6.8135357
MonotonicityNot monotonic
2023-07-08T17:24:21.703713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.641243458 2
 
< 0.1%
5.824251127 2
 
< 0.1%
5.04553566 2
 
< 0.1%
4.427404368 2
 
< 0.1%
7.195165992 2
 
< 0.1%
4.422912264 2
 
< 0.1%
6.121975121 2
 
< 0.1%
7.520288134 2
 
< 0.1%
3.99959445 2
 
< 0.1%
6.821428633 2
 
< 0.1%
Other values (52342) 52468
99.8%
(Missing) 72
 
0.1%
ValueCountFrequency (%)
0.1740378678 1
< 0.1%
0.2303816199 1
< 0.1%
0.2418941408 1
< 0.1%
0.249393905 1
< 0.1%
0.2879439 1
< 0.1%
0.2978626758 1
< 0.1%
0.3236250987 1
< 0.1%
0.3263626242 1
< 0.1%
0.330075185 1
< 0.1%
0.3309189379 1
< 0.1%
ValueCountFrequency (%)
22.93583183 1
< 0.1%
22.66286268 1
< 0.1%
21.26717763 1
< 0.1%
21.11850767 1
< 0.1%
21.11803871 1
< 0.1%
20.73246336 1
< 0.1%
20.42642341 1
< 0.1%
20.02174215 1
< 0.1%
19.83316598 1
< 0.1%
19.35102329 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

Distinct52485
Distinct (%)> 99.9%
Missing73
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean29.054235
Minimum0.47814372
Maximum228.80391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:21.795079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.47814372
5-th percentile3.8999071
Q118.407042
median26.49873
Q337.724222
95-th percentile57.913139
Maximum228.80391
Range228.32576
Interquartile range (IQR)19.317179

Descriptive statistics

Standard deviation16.69434
Coefficient of variation (CV)0.57459233
Kurtosis3.5628202
Mean29.054235
Median Absolute Deviation (MAD)9.378769
Skewness1.2281268
Sum1524969.6
Variance278.701
MonotonicityNot monotonic
2023-07-08T17:24:21.888423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.22486687 2
 
< 0.1%
26.73881536 2
 
< 0.1%
20.53714752 1
 
< 0.1%
19.95674908 1
 
< 0.1%
18.91882977 1
 
< 0.1%
31.24459066 1
 
< 0.1%
27.19142656 1
 
< 0.1%
27.80918205 1
 
< 0.1%
29.25079234 1
 
< 0.1%
35.30718451 1
 
< 0.1%
Other values (52475) 52475
99.8%
(Missing) 73
 
0.1%
ValueCountFrequency (%)
0.4781437218 1
< 0.1%
1.925719147 1
< 0.1%
2.127942796 1
< 0.1%
2.130203724 1
< 0.1%
2.137459932 1
< 0.1%
2.168292549 1
< 0.1%
2.202179432 1
< 0.1%
2.246272024 1
< 0.1%
2.265013959 1
< 0.1%
2.273271241 1
< 0.1%
ValueCountFrequency (%)
228.8039074 1
< 0.1%
181.401366 1
< 0.1%
156.6703199 1
< 0.1%
155.2311286 1
< 0.1%
152.0183294 1
< 0.1%
140.1578468 1
< 0.1%
140.1567275 1
< 0.1%
138.723366 1
< 0.1%
137.6132595 1
< 0.1%
134.4883854 1
< 0.1%

Metal particle count counter
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing73
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean396.57509
Minimum391
Maximum401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size410.8 KiB
2023-07-08T17:24:21.969050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum391
5-th percentile392
Q1394
median398
Q3400
95-th percentile401
Maximum401
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3013254
Coefficient of variation (CV)0.0083245909
Kurtosis-1.4961529
Mean396.57509
Median Absolute Deviation (MAD)3
Skewness-0.056839742
Sum20815037
Variance10.898749
MonotonicityIncreasing
2023-07-08T17:24:22.038105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
394 11614
22.1%
398 9308
17.7%
401 8220
15.6%
400 7548
14.4%
392 5030
9.6%
393 3939
 
7.5%
395 1997
 
3.8%
399 1803
 
3.4%
391 1775
 
3.4%
397 905
 
1.7%
ValueCountFrequency (%)
391 1775
 
3.4%
392 5030
9.6%
393 3939
 
7.5%
394 11614
22.1%
395 1997
 
3.8%
396 348
 
0.7%
397 905
 
1.7%
398 9308
17.7%
399 1803
 
3.4%
400 7548
14.4%
ValueCountFrequency (%)
401 8220
15.6%
400 7548
14.4%
399 1803
 
3.4%
398 9308
17.7%
397 905
 
1.7%
396 348
 
0.7%
395 1997
 
3.8%
394 11614
22.1%
393 3939
 
7.5%
392 5030
9.6%

Interactions

2023-07-08T17:24:17.501890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:03.938928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.079260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.192064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.338972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.471727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.576049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.729828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.859034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.062826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.177209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.262280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.433450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.579422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.019841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.161091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.271141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.413412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.550526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.661941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.810131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.938760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.141779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.254903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.338620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.511024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.666920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.104829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.249429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.365324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.496937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.635938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.751695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.901647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.131799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.231039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.342934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.423397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.596863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.753133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.190907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.339019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.458712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.580682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.723375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.844061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.992444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.219834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.319455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.429870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.508932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.681608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.830334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.266995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.420641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.544482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.654213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.801516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.925176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.074474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.298293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.399588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.506982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.585917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.759760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.913847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.348639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.508156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.633676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.733494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.884533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.015615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.160624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.382557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.483002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.592241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.667363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.840794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:18.002111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.437607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.598259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.731093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.818160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.973472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.106725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.253526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.472624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.574699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.681195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.754188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.930913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:18.089636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.522840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.687429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.829001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.901969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.063158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.202137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.340906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.562079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.665158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.769263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.839038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.015862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:18.176846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.677003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.775527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.926764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.985313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.153166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.294956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.433509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.648451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.755663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.856234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.036728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.103252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:18.262214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.762782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.864390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.014965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.068164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.240504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.386182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.524562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.736320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.843429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.943846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.119632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.187868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:18.343782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.844212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.948430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.096667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.145734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.324405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.474391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.612332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.819013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:13.927845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.023803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.201078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.268978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:18.420720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:04.922009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.029365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.177055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.220557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.407681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.558158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.695701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.899645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.009851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.103938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.276414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.345855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:18.500746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:05.001218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:06.110079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:07.256487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:08.395951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:09.492134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:10.643004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:11.777914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:12.979724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:14.091072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:15.182378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:16.354612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:24:17.421687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:24:22.111554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.1290.132-0.4280.6440.9860.986-0.1970.9240.4790.9820.719-0.025
Wind direction (°)0.1291.0000.892-0.0600.0980.1280.128-0.0320.1300.0440.1210.102-0.093
Nacelle position (°)0.1320.8921.000-0.0590.0980.1320.132-0.0390.1340.0440.1300.103-0.088
blade_angle-0.428-0.060-0.0591.000-0.608-0.409-0.4100.173-0.478-0.147-0.416-0.151-0.047
Rear bearing temperature (°C)0.6440.0980.098-0.6081.0000.6400.6380.0630.7900.3700.6290.4240.050
Rotor speed (RPM)0.9860.1280.132-0.4090.6401.0001.000-0.1850.9200.5230.9650.745-0.019
Generator RPM (RPM)0.9860.1280.132-0.4100.6381.0001.000-0.1910.9200.5220.9650.745-0.019
Nacelle ambient temperature (°C)-0.197-0.032-0.0390.1730.063-0.185-0.1911.000-0.151-0.046-0.182-0.0770.139
Front bearing temperature (°C)0.9240.1300.134-0.4780.7900.9200.920-0.1511.0000.4450.9070.6500.002
Tower Acceleration X (mm/ss)0.4790.0440.044-0.1470.3700.5230.522-0.0460.4451.0000.4260.8410.024
Wind speed (m/s)0.9820.1210.130-0.4160.6290.9650.965-0.1820.9070.4261.0000.678-0.018
Tower Acceleration y (mm/ss)0.7190.1020.103-0.1510.4240.7450.745-0.0770.6500.8410.6781.0000.006
Metal particle count counter-0.025-0.093-0.088-0.0470.050-0.019-0.0190.1390.0020.024-0.0180.0061.000

Missing values

2023-07-08T17:24:18.618498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:24:18.817034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:24:19.051154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02019-01-01 00:00:00211.635132285.916016299.7536010.07516664.5175029.3626101111.8642588.50500064.86750054.7852524.90306920.537148391.0
12019-01-01 00:10:00242.319199292.165405299.7536010.07799964.7249989.5585211136.1539318.50000064.73500181.3487014.80012125.213936391.0
22019-01-01 00:20:00402.834900293.648682299.7536010.00000067.13250010.8298541285.8583988.50000067.72999660.2352035.87143718.700808391.0
32019-01-01 00:30:00358.578827293.884094299.7536010.00000067.62750210.4690801243.4125988.50000068.86000158.8230325.91795418.059677391.0
42019-01-01 00:40:00202.455673305.036804299.7536010.12416666.3200009.3423991110.0096448.46750067.30999884.6463624.55715619.411240391.0
52019-01-01 00:50:00125.681793318.457123299.7536010.64766664.7525029.1800841091.2702648.50000064.62249862.1655123.78627423.358706391.0
62019-01-01 01:00:00258.772369317.305176299.7536010.02483365.9575049.6675001148.6112068.41249965.67500382.6112144.76372026.115253391.0
72019-01-01 01:10:00151.989883309.632996299.7536010.39783365.4400029.3098691105.5955818.40000065.16999869.1501854.30030721.556602391.0
82019-01-01 01:20:00194.871048298.910522299.7536010.14899965.7624979.3507591111.3970958.22250065.21749979.7779544.66320427.522284391.0
92019-01-01 01:30:00158.836700289.073761299.7536010.27849965.2850049.1947721093.2822278.14000064.74250084.4143914.52064429.813000391.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
525502019-12-31 22:20:00208.04187998.926423102.1921540.09266564.4999989.3374921110.4320496.475063.56500155.7263384.82067022.845190401.0
525512019-12-31 22:30:00246.03747799.355834102.1921540.00000065.2099999.4407551120.9632236.402564.81750053.1598035.11919225.241300401.0
525522019-12-31 22:40:00154.97389691.023395102.1921540.24716664.6425009.0422411074.9959516.242564.43750069.7574844.50371528.891085401.0
525532019-12-31 22:50:00199.17613492.477647102.1921540.02483364.4275009.1172811085.3325756.005064.12500048.1181724.71710925.078848401.0
525542019-12-31 23:00:00205.84154795.377971102.1921540.07449964.1299999.2696961101.8304866.000063.82250057.8256704.79486830.499242401.0
525552019-12-31 23:10:00223.29018894.607340102.1921540.02483364.2575009.3097661107.1501295.955063.94500169.3448714.82995131.991540401.0
525562019-12-31 23:20:00203.04471496.724236102.1921540.07466564.0450009.2624321102.1842355.980063.77000169.8366914.90512931.080175401.0
525572019-12-31 23:30:00326.49857397.190545102.1921540.00000065.34750010.1305641203.3134425.947565.37500050.3822015.54804121.947518401.0
525582019-12-31 23:40:00326.126704103.512831102.1921540.00000066.34000110.1629201207.8652845.975067.13750165.3776485.36804122.743052401.0
525592019-12-31 23:50:00282.695367108.850285102.1921540.03216666.1700009.8880321175.4239276.065067.032501123.3735755.37593034.570594401.0